Overview

Dataset statistics

Number of variables8
Number of observations12571
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory785.8 KiB
Average record size in memory64.0 B

Variable types

Numeric8

Alerts

per_area_buildings is highly overall correlated with per_area_greenery and 2 other fieldsHigh correlation
per_area_greenery is highly overall correlated with per_area_buildings and 2 other fieldsHigh correlation
per_residential_road is highly overall correlated with per_area_buildings and 2 other fieldsHigh correlation
per_rural_road is highly overall correlated with per_area_buildings and 2 other fieldsHigh correlation
publictransport_frequency has 3556 (28.3%) zerosZeros
per_area_greenery has 126 (1.0%) zerosZeros
per_area_water has 1419 (11.3%) zerosZeros
per_residential_road has 255 (2.0%) zerosZeros
per_rural_road has 4506 (35.8%) zerosZeros
per_highway has 10632 (84.6%) zerosZeros
per_active has 3970 (31.6%) zerosZeros

Reproduction

Analysis started2024-07-05 13:52:30.017269
Analysis finished2024-07-05 13:52:41.870581
Duration11.85 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

publictransport_frequency
Real number (ℝ)

ZEROS 

Distinct3219
Distinct (%)25.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1113.6817
Minimum0
Maximum102927
Zeros3556
Zeros (%)28.3%
Negative0
Negative (%)0.0%
Memory size98.3 KiB
2024-07-05T15:52:42.085137image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median329
Q31103
95-th percentile4558.5
Maximum102927
Range102927
Interquartile range (IQR)1103

Descriptive statistics

Standard deviation2744.113
Coefficient of variation (CV)2.4640011
Kurtosis226.51133
Mean1113.6817
Median Absolute Deviation (MAD)329
Skewness10.543635
Sum14000093
Variance7530156.1
MonotonicityNot monotonic
2024-07-05T15:52:42.352217image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3556
28.3%
48 34
 
0.3%
108 28
 
0.2%
40 28
 
0.2%
216 27
 
0.2%
312 26
 
0.2%
44 26
 
0.2%
318 25
 
0.2%
156 25
 
0.2%
408 23
 
0.2%
Other values (3209) 8773
69.8%
ValueCountFrequency (%)
0 3556
28.3%
1 4
 
< 0.1%
2 7
 
0.1%
3 1
 
< 0.1%
4 6
 
< 0.1%
5 4
 
< 0.1%
6 5
 
< 0.1%
7 2
 
< 0.1%
8 8
 
0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
102927 1
< 0.1%
53734 1
< 0.1%
53673 1
< 0.1%
52083 1
< 0.1%
46590 1
< 0.1%
44879 1
< 0.1%
42366 1
< 0.1%
39379 1
< 0.1%
36531 1
< 0.1%
35782 1
< 0.1%

per_area_greenery
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12446
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.146473
Minimum0
Maximum38.234096
Zeros126
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size98.3 KiB
2024-07-05T15:52:42.601810image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.98640312
Q14.6143501
median9.4471106
Q321.580973
95-th percentile32.288281
Maximum38.234096
Range38.234096
Interquartile range (IQR)16.966623

Descriptive statistics

Standard deviation10.407929
Coefficient of variation (CV)0.79168977
Kurtosis-0.92229299
Mean13.146473
Median Absolute Deviation (MAD)6.3058627
Skewness0.66216982
Sum165264.32
Variance108.32498
MonotonicityNot monotonic
2024-07-05T15:52:42.885189image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 126
 
1.0%
0.6084135957 1
 
< 0.1%
23.40963449 1
 
< 0.1%
24.32202217 1
 
< 0.1%
23.6284603 1
 
< 0.1%
28.74045 1
 
< 0.1%
7.825149455 1
 
< 0.1%
32.05099836 1
 
< 0.1%
31.77764274 1
 
< 0.1%
28.96790434 1
 
< 0.1%
Other values (12436) 12436
98.9%
ValueCountFrequency (%)
0 126
1.0%
3.311726686 × 10-51
 
< 0.1%
0.0002723611366 1
 
< 0.1%
0.0004865381433 1
 
< 0.1%
0.001162506367 1
 
< 0.1%
0.001353928494 1
 
< 0.1%
0.001474690547 1
 
< 0.1%
0.001575551504 1
 
< 0.1%
0.001637908802 1
 
< 0.1%
0.002747688861 1
 
< 0.1%
ValueCountFrequency (%)
38.23409643 1
< 0.1%
37.04451075 1
< 0.1%
36.45912947 1
< 0.1%
36.38465915 1
< 0.1%
36.3407524 1
< 0.1%
36.3092597 1
< 0.1%
36.2772134 1
< 0.1%
36.26314952 1
< 0.1%
36.20298637 1
< 0.1%
36.12396057 1
< 0.1%

per_area_water
Real number (ℝ)

ZEROS 

Distinct11153
Distinct (%)88.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2789319
Minimum0
Maximum27.855333
Zeros1419
Zeros (%)11.3%
Negative0
Negative (%)0.0%
Memory size98.3 KiB
2024-07-05T15:52:43.101607image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.13216689
median0.69002346
Q31.7932923
95-th percentile4.3308157
Maximum27.855333
Range27.855333
Interquartile range (IQR)1.6611254

Descriptive statistics

Standard deviation1.7785297
Coefficient of variation (CV)1.3906367
Kurtosis23.196309
Mean1.2789319
Median Absolute Deviation (MAD)0.65547501
Skewness3.6280877
Sum16077.453
Variance3.1631679
MonotonicityNot monotonic
2024-07-05T15:52:43.334596image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1419
 
11.3%
1.822359098 1
 
< 0.1%
0.02284018025 1
 
< 0.1%
0.4151801733 1
 
< 0.1%
0.2634600001 1
 
< 0.1%
0.6426504744 1
 
< 0.1%
8.030660101 1
 
< 0.1%
0.1271272136 1
 
< 0.1%
0.003461401315 1
 
< 0.1%
0.01641824603 1
 
< 0.1%
Other values (11143) 11143
88.6%
ValueCountFrequency (%)
0 1419
11.3%
2.63266918 × 10-91
 
< 0.1%
4.321415879 × 10-61
 
< 0.1%
3.323163697 × 10-51
 
< 0.1%
4.429612931 × 10-51
 
< 0.1%
5.712498556 × 10-51
 
< 0.1%
0.0001076551272 1
 
< 0.1%
0.0001555083669 1
 
< 0.1%
0.0002341195121 1
 
< 0.1%
0.0003422481497 1
 
< 0.1%
ValueCountFrequency (%)
27.85533324 1
< 0.1%
20.68321406 1
< 0.1%
20.18943585 1
< 0.1%
20.10695869 1
< 0.1%
19.94603458 1
< 0.1%
19.89993472 1
< 0.1%
19.24829146 1
< 0.1%
19.19504426 1
< 0.1%
18.80182505 1
< 0.1%
17.40978801 1
< 0.1%

per_area_buildings
Real number (ℝ)

HIGH CORRELATION 

Distinct12569
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7170139
Minimum0
Maximum23.944334
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size98.3 KiB
2024-07-05T15:52:43.524837image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.21201936
Q11.2280874
median4.7258947
Q37.0044778
95-th percentile11.04623
Maximum23.944334
Range23.944334
Interquartile range (IQR)5.7763904

Descriptive statistics

Standard deviation3.5930159
Coefficient of variation (CV)0.76171408
Kurtosis0.43506462
Mean4.7170139
Median Absolute Deviation (MAD)2.7844628
Skewness0.68306997
Sum59297.582
Variance12.909763
MonotonicityNot monotonic
2024-07-05T15:52:43.718223image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3
 
< 0.1%
14.61667551 1
 
< 0.1%
2.794422725 1
 
< 0.1%
1.436191297 1
 
< 0.1%
3.61559279 1
 
< 0.1%
0.4251112791 1
 
< 0.1%
7.342691755 1
 
< 0.1%
9.912207701 1
 
< 0.1%
6.481978549 1
 
< 0.1%
3.193982755 1
 
< 0.1%
Other values (12559) 12559
99.9%
ValueCountFrequency (%)
0 3
< 0.1%
0.001292989649 1
 
< 0.1%
0.00346380894 1
 
< 0.1%
0.004593450548 1
 
< 0.1%
0.005046682417 1
 
< 0.1%
0.005332112869 1
 
< 0.1%
0.006402565114 1
 
< 0.1%
0.006660210554 1
 
< 0.1%
0.007209041124 1
 
< 0.1%
0.009291787888 1
 
< 0.1%
ValueCountFrequency (%)
23.94433391 1
< 0.1%
20.90929894 1
< 0.1%
20.73901565 1
< 0.1%
20.71522834 1
< 0.1%
20.52081539 1
< 0.1%
20.44454525 1
< 0.1%
20.27873077 1
< 0.1%
20.17997013 1
< 0.1%
19.900095 1
< 0.1%
19.8617785 1
< 0.1%

per_residential_road
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11160
Distinct (%)88.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.042992
Minimum0
Maximum100
Zeros255
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size98.3 KiB
2024-07-05T15:52:43.935124image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.6759736
Q141.884446
median83.086826
Q395.475011
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)53.590565

Descriptive statistics

Standard deviation33.806344
Coefficient of variation (CV)0.49683799
Kurtosis-0.75851046
Mean68.042992
Median Absolute Deviation (MAD)15.47036
Skewness-0.87768471
Sum855368.45
Variance1142.8689
MonotonicityNot monotonic
2024-07-05T15:52:44.218678image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 1158
 
9.2%
0 255
 
2.0%
6.203252601 1
 
< 0.1%
43.50227604 1
 
< 0.1%
44.06744392 1
 
< 0.1%
57.71250449 1
 
< 0.1%
18.3511672 1
 
< 0.1%
81.09691264 1
 
< 0.1%
43.83238117 1
 
< 0.1%
1.534255003 1
 
< 0.1%
Other values (11150) 11150
88.7%
ValueCountFrequency (%)
0 255
2.0%
2.813046929 × 10-61
 
< 0.1%
6.930776993 × 10-61
 
< 0.1%
1.163882783 × 10-51
 
< 0.1%
5.005113298 × 10-51
 
< 0.1%
6.042829648 × 10-51
 
< 0.1%
9.404111437 × 10-51
 
< 0.1%
0.0006548906082 1
 
< 0.1%
0.001535109314 1
 
< 0.1%
0.002380436417 1
 
< 0.1%
ValueCountFrequency (%)
100 1158
9.2%
99.99993809 1
 
< 0.1%
99.99985334 1
 
< 0.1%
99.99984638 1
 
< 0.1%
99.9997469 1
 
< 0.1%
99.99948226 1
 
< 0.1%
99.9991583 1
 
< 0.1%
99.99891478 1
 
< 0.1%
99.99806817 1
 
< 0.1%
99.99780824 1
 
< 0.1%

per_rural_road
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7910
Distinct (%)62.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.594725
Minimum0
Maximum100
Zeros4506
Zeros (%)35.8%
Negative0
Negative (%)0.0%
Memory size98.3 KiB
2024-07-05T15:52:44.467649image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5.5684527
Q342.03632
95-th percentile92.730106
Maximum100
Range100
Interquartile range (IQR)42.03632

Descriptive statistics

Standard deviation32.169333
Coefficient of variation (CV)1.3634121
Kurtosis-0.12713809
Mean23.594725
Median Absolute Deviation (MAD)5.5684527
Skewness1.1714523
Sum296609.29
Variance1034.866
MonotonicityNot monotonic
2024-07-05T15:52:44.734890image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4506
35.8%
100 157
 
1.2%
5.424696772 1
 
< 0.1%
50.26381274 1
 
< 0.1%
77.16867586 1
 
< 0.1%
45.76869223 1
 
< 0.1%
53.37341233 1
 
< 0.1%
88.26858991 1
 
< 0.1%
9.281257834 1
 
< 0.1%
86.58530809 1
 
< 0.1%
Other values (7900) 7900
62.8%
ValueCountFrequency (%)
0 4506
35.8%
3.303356718 × 10-71
 
< 0.1%
5.173885353 × 10-71
 
< 0.1%
1.401814492 × 10-61
 
< 0.1%
5.507150298 × 10-61
 
< 0.1%
7.227010468 × 10-61
 
< 0.1%
9.870222105 × 10-61
 
< 0.1%
2.319624833 × 10-51
 
< 0.1%
3.0197218 × 10-51
 
< 0.1%
6.19128133 × 10-51
 
< 0.1%
ValueCountFrequency (%)
100 157
1.2%
99.99999307 1
 
< 0.1%
99.99997018 1
 
< 0.1%
99.99954416 1
 
< 0.1%
99.99846489 1
 
< 0.1%
99.99761956 1
 
< 0.1%
99.99709803 1
 
< 0.1%
99.99653853 1
 
< 0.1%
99.99597167 1
 
< 0.1%
99.99534953 1
 
< 0.1%

per_highway
Real number (ℝ)

ZEROS 

Distinct1940
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5633609
Minimum0
Maximum82.446436
Zeros10632
Zeros (%)84.6%
Negative0
Negative (%)0.0%
Memory size98.3 KiB
2024-07-05T15:52:45.000725image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile20.580885
Maximum82.446436
Range82.446436
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.9658349
Coefficient of variation (CV)3.1075745
Kurtosis18.031955
Mean2.5633609
Median Absolute Deviation (MAD)0
Skewness3.937663
Sum32224.01
Variance63.454526
MonotonicityNot monotonic
2024-07-05T15:52:45.201071image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10632
84.6%
16.20760746 1
 
< 0.1%
14.15066842 1
 
< 0.1%
16.48124845 1
 
< 0.1%
14.44114321 1
 
< 0.1%
25.96734074 1
 
< 0.1%
2.69963976 1
 
< 0.1%
10.50392443 1
 
< 0.1%
12.05509345 1
 
< 0.1%
15.79913184 1
 
< 0.1%
Other values (1930) 1930
 
15.4%
ValueCountFrequency (%)
0 10632
84.6%
2.53541011 × 10-81
 
< 0.1%
1.291196562 × 10-51
 
< 0.1%
2.981951659 × 10-51
 
< 0.1%
0.0009844463095 1
 
< 0.1%
0.004987036131 1
 
< 0.1%
0.005559388174 1
 
< 0.1%
0.006223532915 1
 
< 0.1%
0.009857536939 1
 
< 0.1%
0.01030284224 1
 
< 0.1%
ValueCountFrequency (%)
82.44643577 1
< 0.1%
82.29696542 1
< 0.1%
81.45329661 1
< 0.1%
74.10008756 1
< 0.1%
72.70205204 1
< 0.1%
71.57418414 1
< 0.1%
71.55667891 1
< 0.1%
69.41308759 1
< 0.1%
66.58743442 1
< 0.1%
66.42356028 1
< 0.1%

per_active
Real number (ℝ)

ZEROS 

Distinct8602
Distinct (%)68.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7750576
Minimum0
Maximum85.649963
Zeros3970
Zeros (%)31.6%
Negative0
Negative (%)0.0%
Memory size98.3 KiB
2024-07-05T15:52:45.450677image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.9352653
Q37.8894786
95-th percentile23.984893
Maximum85.649963
Range85.649963
Interquartile range (IQR)7.8894786

Descriptive statistics

Standard deviation9.0378316
Coefficient of variation (CV)1.5649769
Kurtosis9.7579215
Mean5.7750576
Median Absolute Deviation (MAD)1.9352653
Skewness2.676297
Sum72598.249
Variance81.6824
MonotonicityNot monotonic
2024-07-05T15:52:45.667232image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3970
31.6%
2.36457699 1
 
< 0.1%
8.722710414 1
 
< 0.1%
8.555583501 1
 
< 0.1%
0.05267591937 1
 
< 0.1%
1.65732139 1
 
< 0.1%
3.01276976 1
 
< 0.1%
3.847046672 1
 
< 0.1%
1.394805191 1
 
< 0.1%
5.119559088 1
 
< 0.1%
Other values (8592) 8592
68.3%
ValueCountFrequency (%)
0 3970
31.6%
1.906369148 × 10-81
 
< 0.1%
2.407957919 × 10-61
 
< 0.1%
4.186213212 × 10-61
 
< 0.1%
6.195031865 × 10-61
 
< 0.1%
1.077248401 × 10-51
 
< 0.1%
1.373922802 × 10-51
 
< 0.1%
1.679919842 × 10-51
 
< 0.1%
2.887271467 × 10-51
 
< 0.1%
2.962671885 × 10-51
 
< 0.1%
ValueCountFrequency (%)
85.64996278 1
< 0.1%
83.65960131 1
< 0.1%
78.76944169 1
< 0.1%
73.48812014 1
< 0.1%
73.21171981 1
< 0.1%
72.91684443 1
< 0.1%
72.71952305 1
< 0.1%
72.43716033 1
< 0.1%
70.52574471 1
< 0.1%
69.67614596 1
< 0.1%

Interactions

2024-07-05T15:52:39.988848image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:30.524176image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:32.055386image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:33.539753image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:34.938972image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:36.188504image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:37.370211image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:38.613945image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:40.120410image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:30.737818image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:32.355659image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:33.721414image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:35.089553image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:36.341050image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:37.540742image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:38.773115image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:40.268926image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:30.976023image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:32.541796image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:33.871674image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:35.238487image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:36.511623image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:37.704797image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:38.937665image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:40.602420image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:31.190615image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:32.721495image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:34.023076image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:35.410555image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:36.654873image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:37.859126image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:39.140070image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:40.753766image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:31.364081image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:32.865152image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:34.233350image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:35.544806image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:36.792524image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:38.003582image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:39.290252image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:40.925688image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:31.524258image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:33.007654image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:34.404666image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:35.688464image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:36.921757image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:38.188365image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:39.487935image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:41.103547image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:31.707243image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:33.173896image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:34.589393image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:35.837611image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:37.065572image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:38.336511image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:39.636422image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:41.246902image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:31.898811image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:33.324400image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:34.788892image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:36.044218image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:37.188109image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:38.471137image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:52:39.804134image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-07-05T15:52:45.825246image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
per_activeper_area_buildingsper_area_greeneryper_area_waterper_highwayper_residential_roadper_rural_roadpublictransport_frequency
per_active1.0000.035-0.0690.2000.019-0.188-0.1760.129
per_area_buildings0.0351.000-0.884-0.061-0.2740.741-0.7620.166
per_area_greenery-0.069-0.8841.000-0.0410.257-0.7250.760-0.142
per_area_water0.200-0.061-0.0411.0000.063-0.049-0.0520.044
per_highway0.019-0.2740.2570.0631.000-0.3320.1980.047
per_residential_road-0.1880.741-0.725-0.049-0.3321.000-0.8440.127
per_rural_road-0.176-0.7620.760-0.0520.198-0.8441.000-0.149
publictransport_frequency0.1290.166-0.1420.0440.0470.127-0.1491.000

Missing values

2024-07-05T15:52:41.468509image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-05T15:52:41.751886image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

publictransport_frequencyper_area_greeneryper_area_waterper_area_buildingsper_residential_roadper_rural_roadper_highwayper_active
02826.00.6084141.82235914.616676100.0000000.0000000.00.000000
113625.01.3546912.36926614.756267100.0000000.0000000.00.000000
25079.01.0371222.92351412.72429691.1367420.0000000.08.863258
31215.00.0000003.49846816.38070898.9188280.0000000.01.081172
42474.01.6523441.20766812.163932100.0000000.0000000.00.000000
514103.00.9346074.8410935.350556100.0000000.0000000.00.000000
62243.05.2625882.2477944.85703279.51923610.7526900.09.728074
70.01.2085811.85950510.98348492.9545006.0896260.00.955875
80.02.2998550.0000008.83180177.85384922.1461510.00.000000
92782.04.0697171.0901886.52688787.4259588.8176750.03.756367
publictransport_frequencyper_area_greeneryper_area_waterper_area_buildingsper_residential_roadper_rural_roadper_highwayper_active
125610.016.4321380.7861373.64851560.28705125.5695020.00000014.143447
125620.021.7952702.2817211.79348660.59633222.2007920.00000017.202876
12563666.032.9146240.6271100.26569811.02101569.81102415.8579483.310013
1256442.06.9947380.1055864.61257589.89712910.1028710.0000000.000000
1256563.014.7600560.0317232.64454086.1078298.7953970.0000005.096774
125660.07.3874270.0000005.46018983.7420982.4610160.00000013.796886
1256716.015.6511231.5255002.02098579.27744716.2874950.0000004.435058
1256816.08.1504960.6416424.00099034.68288965.3171110.0000000.000000
1256916.018.5705460.8237811.82934483.45789711.5938410.0000004.948262
1257084.032.8260940.3437910.21575816.05765766.3410080.00000017.601335